Overview

Dataset statistics

Number of variables28
Number of observations18905
Missing cells34457
Missing cells (%)6.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 MiB
Average record size in memory224.0 B

Variable types

Text1
Categorical5
Numeric17
Boolean5

Alerts

squareMeters is highly overall correlated with rooms and 1 other fieldsHigh correlation
rooms is highly overall correlated with squareMetersHigh correlation
floor is highly overall correlated with floorCountHigh correlation
floorCount is highly overall correlated with floor and 2 other fieldsHigh correlation
buildYear is highly overall correlated with type and 2 other fieldsHigh correlation
latitude is highly overall correlated with cityHigh correlation
longitude is highly overall correlated with cityHigh correlation
centreDistance is highly overall correlated with poiCount and 1 other fieldsHigh correlation
poiCount is highly overall correlated with centreDistance and 6 other fieldsHigh correlation
schoolDistance is highly overall correlated with poiCountHigh correlation
clinicDistance is highly overall correlated with poiCountHigh correlation
postOfficeDistance is highly overall correlated with poiCountHigh correlation
restaurantDistance is highly overall correlated with poiCountHigh correlation
collegeDistance is highly overall correlated with centreDistance and 1 other fieldsHigh correlation
pharmacyDistance is highly overall correlated with poiCountHigh correlation
price is highly overall correlated with squareMetersHigh correlation
city is highly overall correlated with latitude and 1 other fieldsHigh correlation
type is highly overall correlated with buildYear and 1 other fieldsHigh correlation
buildingMaterial is highly overall correlated with floorCount and 2 other fieldsHigh correlation
condition is highly overall correlated with buildYearHigh correlation
hasElevator is highly overall correlated with floorCountHigh correlation
ownership is highly imbalanced (51.0%)Imbalance
hasSecurity is highly imbalanced (52.5%)Imbalance
type has 4039 (21.4%) missing valuesMissing
floor has 3438 (18.2%) missing valuesMissing
floorCount has 262 (1.4%) missing valuesMissing
buildYear has 3271 (17.3%) missing valuesMissing
collegeDistance has 565 (3.0%) missing valuesMissing
buildingMaterial has 7387 (39.1%) missing valuesMissing
condition has 14344 (75.9%) missing valuesMissing
hasElevator has 926 (4.9%) missing valuesMissing
id has unique valuesUnique
poiCount has 997 (5.3%) zerosZeros

Reproduction

Analysis started2024-03-17 16:01:30.292656
Analysis finished2024-03-17 16:01:55.448231
Duration25.16 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

id
Text

UNIQUE 

Distinct18905
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size147.8 KiB
2024-03-17T17:01:55.563469image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters604960
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18905 ?
Unique (%)100.0%

Sample

1st rowf8524536d4b09a0c8ccc0197ec9d7bde
2nd rowaccbe77d4b360fea9735f138a50608dd
3rd row8373aa373dbc3fe7ca3b7434166b8766
4th row0a68cd14c44ec5140143ece75d739535
5th rowf66320e153c2441edc0fe293b54c8aeb
ValueCountFrequency (%)
f8524536d4b09a0c8ccc0197ec9d7bde 1
 
< 0.1%
1f481e8bf65b789b5d78a426268c8ee2 1
 
< 0.1%
0a68cd14c44ec5140143ece75d739535 1
 
< 0.1%
f66320e153c2441edc0fe293b54c8aeb 1
 
< 0.1%
2e190fcd6934978ca36d86ba41e842fc 1
 
< 0.1%
ec27024bfcd012728617a35dad2cb6b8 1
 
< 0.1%
d3e0e36529df3360849ec40168c10755 1
 
< 0.1%
7e1981e920d763d6237c5bdcf13cf5b7 1
 
< 0.1%
4a04a9c54d8281e3ec23df031e538d85 1
 
< 0.1%
c02c8dfec79305a35f639e0a5226802a 1
 
< 0.1%
Other values (18895) 18895
99.9%
2024-03-17T17:01:55.812968image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 38175
 
6.3%
a 37981
 
6.3%
3 37978
 
6.3%
7 37939
 
6.3%
e 37881
 
6.3%
c 37808
 
6.2%
4 37800
 
6.2%
5 37791
 
6.2%
f 37782
 
6.2%
2 37782
 
6.2%
Other values (6) 226043
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 377570
62.4%
Lowercase Letter 227390
37.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 37978
10.1%
7 37939
10.0%
4 37800
10.0%
5 37791
10.0%
2 37782
10.0%
0 37779
10.0%
6 37741
10.0%
8 37688
10.0%
9 37558
9.9%
1 37514
9.9%
Lowercase Letter
ValueCountFrequency (%)
d 38175
16.8%
a 37981
16.7%
e 37881
16.7%
c 37808
16.6%
f 37782
16.6%
b 37763
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 377570
62.4%
Latin 227390
37.6%

Most frequent character per script

Common
ValueCountFrequency (%)
3 37978
10.1%
7 37939
10.0%
4 37800
10.0%
5 37791
10.0%
2 37782
10.0%
0 37779
10.0%
6 37741
10.0%
8 37688
10.0%
9 37558
9.9%
1 37514
9.9%
Latin
ValueCountFrequency (%)
d 38175
16.8%
a 37981
16.7%
e 37881
16.7%
c 37808
16.6%
f 37782
16.6%
b 37763
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 604960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 38175
 
6.3%
a 37981
 
6.3%
3 37978
 
6.3%
7 37939
 
6.3%
e 37881
 
6.3%
c 37808
 
6.2%
4 37800
 
6.2%
5 37791
 
6.2%
f 37782
 
6.2%
2 37782
 
6.2%
Other values (6) 226043
37.4%

city
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size147.8 KiB
warszawa
5471 
krakow
2983 
gdansk
1898 
wroclaw
1768 
lodz
1555 
Other values (10)
5230 

Length

Max length11
Median length9
Mean length6.8481354
Min length4

Characters and Unicode

Total characters129464
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowszczecin
2nd rowszczecin
3rd rowszczecin
4th rowszczecin
5th rowszczecin

Common Values

ValueCountFrequency (%)
warszawa 5471
28.9%
krakow 2983
15.8%
gdansk 1898
 
10.0%
wroclaw 1768
 
9.4%
lodz 1555
 
8.2%
bydgoszcz 941
 
5.0%
gdynia 830
 
4.4%
poznan 773
 
4.1%
lublin 691
 
3.7%
szczecin 552
 
2.9%
Other values (5) 1443
 
7.6%

Length

2024-03-17T17:01:55.931992image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
warszawa 5471
28.9%
krakow 2983
15.8%
gdansk 1898
 
10.0%
wroclaw 1768
 
9.4%
lodz 1555
 
8.2%
bydgoszcz 941
 
5.0%
gdynia 830
 
4.4%
poznan 773
 
4.1%
lublin 691
 
3.7%
szczecin 552
 
2.9%
Other values (5) 1443
 
7.6%

Most occurring characters

ValueCountFrequency (%)
a 25914
20.0%
w 18318
14.1%
z 11348
8.8%
r 10740
8.3%
o 9638
 
7.4%
s 9493
 
7.3%
k 8614
 
6.7%
d 5548
 
4.3%
n 5517
 
4.3%
l 4967
 
3.8%
Other values (11) 19367
15.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 129464
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 25914
20.0%
w 18318
14.1%
z 11348
8.8%
r 10740
8.3%
o 9638
 
7.4%
s 9493
 
7.3%
k 8614
 
6.7%
d 5548
 
4.3%
n 5517
 
4.3%
l 4967
 
3.8%
Other values (11) 19367
15.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 129464
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 25914
20.0%
w 18318
14.1%
z 11348
8.8%
r 10740
8.3%
o 9638
 
7.4%
s 9493
 
7.3%
k 8614
 
6.7%
d 5548
 
4.3%
n 5517
 
4.3%
l 4967
 
3.8%
Other values (11) 19367
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 25914
20.0%
w 18318
14.1%
z 11348
8.8%
r 10740
8.3%
o 9638
 
7.4%
s 9493
 
7.3%
k 8614
 
6.7%
d 5548
 
4.3%
n 5517
 
4.3%
l 4967
 
3.8%
Other values (11) 19367
15.0%

type
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing4039
Missing (%)21.4%
Memory size147.8 KiB
blockOfFlats
9089 
tenement
2895 
apartmentBuilding
2882 

Length

Max length17
Median length12
Mean length12.190367
Min length8

Characters and Unicode

Total characters181222
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowblockOfFlats
2nd rowblockOfFlats
3rd rowtenement
4th rowtenement
5th rowblockOfFlats

Common Values

ValueCountFrequency (%)
blockOfFlats 9089
48.1%
tenement 2895
 
15.3%
apartmentBuilding 2882
 
15.2%
(Missing) 4039
21.4%

Length

2024-03-17T17:01:56.024211image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-17T17:01:56.108395image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
blockofflats 9089
61.1%
tenement 2895
 
19.5%
apartmentbuilding 2882
 
19.4%

Most occurring characters

ValueCountFrequency (%)
l 21060
 
11.6%
t 20643
 
11.4%
a 14853
 
8.2%
e 11567
 
6.4%
n 11554
 
6.4%
b 9089
 
5.0%
F 9089
 
5.0%
s 9089
 
5.0%
f 9089
 
5.0%
O 9089
 
5.0%
Other values (11) 56100
31.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 160162
88.4%
Uppercase Letter 21060
 
11.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 21060
13.1%
t 20643
12.9%
a 14853
9.3%
e 11567
 
7.2%
n 11554
 
7.2%
b 9089
 
5.7%
s 9089
 
5.7%
f 9089
 
5.7%
k 9089
 
5.7%
c 9089
 
5.7%
Other values (8) 35040
21.9%
Uppercase Letter
ValueCountFrequency (%)
F 9089
43.2%
O 9089
43.2%
B 2882
 
13.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 181222
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 21060
 
11.6%
t 20643
 
11.4%
a 14853
 
8.2%
e 11567
 
6.4%
n 11554
 
6.4%
b 9089
 
5.0%
F 9089
 
5.0%
s 9089
 
5.0%
f 9089
 
5.0%
O 9089
 
5.0%
Other values (11) 56100
31.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 181222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 21060
 
11.6%
t 20643
 
11.4%
a 14853
 
8.2%
e 11567
 
6.4%
n 11554
 
6.4%
b 9089
 
5.0%
F 9089
 
5.0%
s 9089
 
5.0%
f 9089
 
5.0%
O 9089
 
5.0%
Other values (11) 56100
31.0%

squareMeters
Real number (ℝ)

HIGH CORRELATION 

Distinct4214
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.509976
Minimum25
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2024-03-17T17:01:56.207733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile31.65
Q144.2
median54.4
Q368.2
95-th percentile100
Maximum150
Range125
Interquartile range (IQR)24

Descriptive statistics

Standard deviation21.060168
Coefficient of variation (CV)0.35994149
Kurtosis1.8737914
Mean58.509976
Median Absolute Deviation (MAD)11.64
Skewness1.1917799
Sum1106131.1
Variance443.53068
MonotonicityNot monotonic
2024-03-17T17:01:56.321178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 249
 
1.3%
38 245
 
1.3%
53 233
 
1.2%
47 228
 
1.2%
49 216
 
1.1%
50 213
 
1.1%
60 206
 
1.1%
45 199
 
1.1%
46 189
 
1.0%
37 188
 
1.0%
Other values (4204) 16739
88.5%
ValueCountFrequency (%)
25 48
0.3%
25.02 1
 
< 0.1%
25.03 2
 
< 0.1%
25.04 5
 
< 0.1%
25.07 1
 
< 0.1%
25.1 2
 
< 0.1%
25.11 1
 
< 0.1%
25.12 1
 
< 0.1%
25.2 1
 
< 0.1%
25.22 1
 
< 0.1%
ValueCountFrequency (%)
150 4
< 0.1%
149.94 1
 
< 0.1%
149.86 1
 
< 0.1%
149.8 1
 
< 0.1%
149.7 1
 
< 0.1%
149.2 2
< 0.1%
149.03 1
 
< 0.1%
149 2
< 0.1%
148.9 1
 
< 0.1%
148.67 1
 
< 0.1%

rooms
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6767522
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2024-03-17T17:01:56.409101image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.90361902
Coefficient of variation (CV)0.33758038
Kurtosis0.70906639
Mean2.6767522
Median Absolute Deviation (MAD)1
Skewness0.63658733
Sum50604
Variance0.81652733
MonotonicityNot monotonic
2024-03-17T17:01:56.486087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 7671
40.6%
3 7157
37.9%
4 2401
 
12.7%
1 1080
 
5.7%
5 469
 
2.5%
6 127
 
0.7%
ValueCountFrequency (%)
1 1080
 
5.7%
2 7671
40.6%
3 7157
37.9%
4 2401
 
12.7%
5 469
 
2.5%
6 127
 
0.7%
ValueCountFrequency (%)
6 127
 
0.7%
5 469
 
2.5%
4 2401
 
12.7%
3 7157
37.9%
2 7671
40.6%
1 1080
 
5.7%

floor
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)0.2%
Missing3438
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean3.3398203
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2024-03-17T17:01:56.566384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile9
Maximum29
Range28
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.5491408
Coefficient of variation (CV)0.76325688
Kurtosis6.9692679
Mean3.3398203
Median Absolute Deviation (MAD)1
Skewness2.0888164
Sum51657
Variance6.4981188
MonotonicityNot monotonic
2024-03-17T17:01:56.656173image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 3715
19.7%
3 3240
17.1%
2 3148
16.7%
4 2335
12.4%
5 889
 
4.7%
6 511
 
2.7%
7 396
 
2.1%
8 363
 
1.9%
9 288
 
1.5%
10 281
 
1.5%
Other values (14) 301
 
1.6%
(Missing) 3438
18.2%
ValueCountFrequency (%)
1 3715
19.7%
2 3148
16.7%
3 3240
17.1%
4 2335
12.4%
5 889
 
4.7%
6 511
 
2.7%
7 396
 
2.1%
8 363
 
1.9%
9 288
 
1.5%
10 281
 
1.5%
ValueCountFrequency (%)
29 3
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
 
< 0.1%
19 2
 
< 0.1%
18 5
 
< 0.1%
17 23
0.1%
16 5
 
< 0.1%
15 31
0.2%

floorCount
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)0.2%
Missing262
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean5.2797833
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2024-03-17T17:01:56.747029image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q36
95-th percentile11
Maximum29
Range28
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.3336658
Coefficient of variation (CV)0.63140201
Kurtosis4.6059215
Mean5.2797833
Median Absolute Deviation (MAD)1
Skewness1.8384478
Sum98431
Variance11.113327
MonotonicityNot monotonic
2024-03-17T17:01:56.842220image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4 6051
32.0%
3 3398
18.0%
5 1819
 
9.6%
10 1731
 
9.2%
2 1600
 
8.5%
6 919
 
4.9%
7 614
 
3.2%
11 523
 
2.8%
8 496
 
2.6%
1 448
 
2.4%
Other values (19) 1044
 
5.5%
(Missing) 262
 
1.4%
ValueCountFrequency (%)
1 448
 
2.4%
2 1600
 
8.5%
3 3398
18.0%
4 6051
32.0%
5 1819
 
9.6%
6 919
 
4.9%
7 614
 
3.2%
8 496
 
2.6%
9 235
 
1.2%
10 1731
 
9.2%
ValueCountFrequency (%)
29 4
 
< 0.1%
28 3
 
< 0.1%
27 4
 
< 0.1%
26 2
 
< 0.1%
25 5
 
< 0.1%
24 14
0.1%
23 11
0.1%
22 13
0.1%
21 4
 
< 0.1%
20 10
0.1%

buildYear
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct155
Distinct (%)1.0%
Missing3271
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean1984.551
Minimum1850
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2024-03-17T17:01:56.950098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1850
5-th percentile1912
Q11967
median1990
Q32014
95-th percentile2022
Maximum2023
Range173
Interquartile range (IQR)47

Descriptive statistics

Standard deviation33.767929
Coefficient of variation (CV)0.017015399
Kurtosis0.10442682
Mean1984.551
Median Absolute Deviation (MAD)24
Skewness-0.88120736
Sum31026471
Variance1140.273
MonotonicityNot monotonic
2024-03-17T17:01:57.063788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2022 966
 
5.1%
1980 738
 
3.9%
1970 715
 
3.8%
2023 702
 
3.7%
2021 401
 
2.1%
1960 398
 
2.1%
1930 361
 
1.9%
2017 338
 
1.8%
2008 323
 
1.7%
2020 320
 
1.7%
Other values (145) 10372
54.9%
(Missing) 3271
 
17.3%
ValueCountFrequency (%)
1850 1
 
< 0.1%
1851 1
 
< 0.1%
1852 1
 
< 0.1%
1854 2
< 0.1%
1855 1
 
< 0.1%
1860 2
< 0.1%
1865 2
< 0.1%
1867 1
 
< 0.1%
1870 4
< 0.1%
1872 1
 
< 0.1%
ValueCountFrequency (%)
2023 702
3.7%
2022 966
5.1%
2021 401
2.1%
2020 320
 
1.7%
2019 266
 
1.4%
2018 297
 
1.6%
2017 338
 
1.8%
2016 315
 
1.7%
2015 205
 
1.1%
2014 195
 
1.0%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct13762
Distinct (%)72.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.020411
Minimum49.97911
Maximum54.58321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2024-03-17T17:01:57.177680image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum49.97911
5-th percentile50.041681
Q151.1091
median52.193604
Q352.412561
95-th percentile54.412046
Maximum54.58321
Range4.6041
Interquartile range (IQR)1.3034607

Descriptive statistics

Standard deviation1.3539981
Coefficient of variation (CV)0.026028208
Kurtosis-0.68353134
Mean52.020411
Median Absolute Deviation (MAD)0.94681778
Skewness0.24821238
Sum983445.87
Variance1.8333108
MonotonicityNot monotonic
2024-03-17T17:01:57.296362image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.126 123
 
0.7%
53.1249837 40
 
0.2%
52.2333751 36
 
0.2%
53.1081 34
 
0.2%
52.194157 33
 
0.2%
51.7636 33
 
0.2%
52.2296756 31
 
0.2%
52.2463 31
 
0.2%
52.1378544 31
 
0.2%
53.1398539 29
 
0.2%
Other values (13752) 18484
97.8%
ValueCountFrequency (%)
49.97911 1
< 0.1%
49.98135663 1
< 0.1%
49.98245549 1
< 0.1%
49.982778 2
< 0.1%
49.9837508 1
< 0.1%
49.9840353 1
< 0.1%
49.9841 1
< 0.1%
49.9846339 1
< 0.1%
49.98539753 1
< 0.1%
49.988159 1
< 0.1%
ValueCountFrequency (%)
54.58321 1
< 0.1%
54.580868 1
< 0.1%
54.57976778 1
< 0.1%
54.57922 1
< 0.1%
54.57558 1
< 0.1%
54.57529 1
< 0.1%
54.5716 1
< 0.1%
54.5715 1
< 0.1%
54.56908 1
< 0.1%
54.5689 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct14016
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.516394
Minimum14.447127
Maximum23.207128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2024-03-17T17:01:57.406565image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum14.447127
5-th percentile16.916924
Q118.53622
median19.908944
Q320.99201
95-th percentile22.4816
Maximum23.207128
Range8.7600013
Interquartile range (IQR)2.45579

Descriptive statistics

Standard deviation1.7690529
Coefficient of variation (CV)0.090644455
Kurtosis0.16917804
Mean19.516394
Median Absolute Deviation (MAD)1.147356
Skewness-0.56504635
Sum368957.44
Variance3.1295483
MonotonicityNot monotonic
2024-03-17T17:01:57.511684image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0079 123
 
0.7%
18.0497523 40
 
0.2%
20.9571835 36
 
0.2%
18.0339 34
 
0.2%
19.4802 33
 
0.2%
21.0346955 33
 
0.2%
21.0122287 31
 
0.2%
21.0291229 31
 
0.2%
21.0695 30
 
0.2%
18.0257721 29
 
0.2%
Other values (14006) 18485
97.8%
ValueCountFrequency (%)
14.4471272 1
< 0.1%
14.462282 1
< 0.1%
14.4779792 1
< 0.1%
14.4787373 1
< 0.1%
14.4807861 1
< 0.1%
14.481843 1
< 0.1%
14.4830325 2
< 0.1%
14.4831 1
< 0.1%
14.483117 1
< 0.1%
14.48342 1
< 0.1%
ValueCountFrequency (%)
23.2071285 2
< 0.1%
23.1957006 1
 
< 0.1%
23.1953627 1
 
< 0.1%
23.19146 1
 
< 0.1%
23.19077 1
 
< 0.1%
23.1897944 3
< 0.1%
23.18956216 1
 
< 0.1%
23.18925905 1
 
< 0.1%
23.18679 1
 
< 0.1%
23.18634 1
 
< 0.1%

centreDistance
Real number (ℝ)

HIGH CORRELATION 

Distinct1311
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3984438
Minimum0.02
Maximum16.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2024-03-17T17:01:57.615860image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.65
Q12.07
median4.01
Q36.18
95-th percentile9.78
Maximum16.94
Range16.92
Interquartile range (IQR)4.11

Descriptive statistics

Standard deviation2.8573874
Coefficient of variation (CV)0.64963599
Kurtosis0.54208751
Mean4.3984438
Median Absolute Deviation (MAD)2.03
Skewness0.82470973
Sum83152.58
Variance8.1646626
MonotonicityNot monotonic
2024-03-17T17:01:57.728282image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.65 131
 
0.7%
3.29 72
 
0.4%
3.16 58
 
0.3%
2.7 56
 
0.3%
2.66 55
 
0.3%
4.68 54
 
0.3%
1.32 54
 
0.3%
4.77 53
 
0.3%
1.79 53
 
0.3%
1.27 52
 
0.3%
Other values (1301) 18267
96.6%
ValueCountFrequency (%)
0.02 1
 
< 0.1%
0.03 3
< 0.1%
0.04 4
< 0.1%
0.05 2
 
< 0.1%
0.06 1
 
< 0.1%
0.07 4
< 0.1%
0.08 2
 
< 0.1%
0.09 3
< 0.1%
0.1 6
< 0.1%
0.11 2
 
< 0.1%
ValueCountFrequency (%)
16.94 1
< 0.1%
16.91 1
< 0.1%
16.82 2
< 0.1%
16.75 1
< 0.1%
16.62 1
< 0.1%
16.47 1
< 0.1%
16.37 1
< 0.1%
16.36 2
< 0.1%
16.13 1
< 0.1%
15.96 1
< 0.1%

poiCount
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct167
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.099656
Minimum0
Maximum208
Zeros997
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2024-03-17T17:01:57.847915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median13
Q323
95-th percentile67
Maximum208
Range208
Interquartile range (IQR)16

Descriptive statistics

Standard deviation23.513874
Coefficient of variation (CV)1.1698645
Kurtosis10.617813
Mean20.099656
Median Absolute Deviation (MAD)8
Skewness2.868392
Sum379984
Variance552.90226
MonotonicityNot monotonic
2024-03-17T17:01:57.961156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 997
 
5.3%
12 764
 
4.0%
9 763
 
4.0%
7 743
 
3.9%
6 735
 
3.9%
8 726
 
3.8%
11 698
 
3.7%
10 631
 
3.3%
13 627
 
3.3%
4 626
 
3.3%
Other values (157) 11595
61.3%
ValueCountFrequency (%)
0 997
5.3%
1 578
3.1%
2 538
2.8%
3 618
3.3%
4 626
3.3%
5 593
3.1%
6 735
3.9%
7 743
3.9%
8 726
3.8%
9 763
4.0%
ValueCountFrequency (%)
208 1
 
< 0.1%
206 2
 
< 0.1%
202 4
< 0.1%
196 2
 
< 0.1%
194 3
 
< 0.1%
192 1
 
< 0.1%
186 1
 
< 0.1%
177 8
< 0.1%
176 1
 
< 0.1%
171 4
< 0.1%

schoolDistance
Real number (ℝ)

HIGH CORRELATION 

Distinct1645
Distinct (%)8.7%
Missing14
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.41489715
Minimum0.004
Maximum4.818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2024-03-17T17:01:58.072354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.004
5-th percentile0.081
Q10.175
median0.288
Q30.4665
95-th percentile1.139
Maximum4.818
Range4.814
Interquartile range (IQR)0.2915

Descriptive statistics

Standard deviation0.47278239
Coefficient of variation (CV)1.1395171
Kurtosis25.369934
Mean0.41489715
Median Absolute Deviation (MAD)0.131
Skewness4.322157
Sum7837.822
Variance0.22352319
MonotonicityNot monotonic
2024-03-17T17:01:58.183153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 163
 
0.9%
0.206 81
 
0.4%
0.243 78
 
0.4%
0.324 77
 
0.4%
0.179 75
 
0.4%
0.301 74
 
0.4%
0.285 72
 
0.4%
0.143 69
 
0.4%
0.216 67
 
0.4%
0.251 66
 
0.3%
Other values (1635) 18069
95.6%
ValueCountFrequency (%)
0.004 3
< 0.1%
0.005 1
 
< 0.1%
0.006 1
 
< 0.1%
0.007 4
< 0.1%
0.009 1
 
< 0.1%
0.011 2
 
< 0.1%
0.012 1
 
< 0.1%
0.013 2
 
< 0.1%
0.014 4
< 0.1%
0.015 5
< 0.1%
ValueCountFrequency (%)
4.818 1
< 0.1%
4.797 1
< 0.1%
4.762 1
< 0.1%
4.692 2
< 0.1%
4.689 1
< 0.1%
4.672 1
< 0.1%
4.658 1
< 0.1%
4.517 1
< 0.1%
4.495 1
< 0.1%
4.406 1
< 0.1%

clinicDistance
Real number (ℝ)

HIGH CORRELATION 

Distinct3208
Distinct (%)17.0%
Missing88
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1.0021962
Minimum0.005
Maximum4.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2024-03-17T17:01:58.289928image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.005
5-th percentile0.131
Q10.367
median0.7
Q31.286
95-th percentile3.0784
Maximum4.996
Range4.991
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.91372099
Coefficient of variation (CV)0.91171867
Kurtosis2.2482273
Mean1.0021962
Median Absolute Deviation (MAD)0.392
Skewness1.6152609
Sum18858.326
Variance0.83488605
MonotonicityNot monotonic
2024-03-17T17:01:58.401990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.205 137
 
0.7%
0.417 62
 
0.3%
0.255 53
 
0.3%
0.326 52
 
0.3%
0.902 51
 
0.3%
0.131 50
 
0.3%
0.313 49
 
0.3%
0.735 41
 
0.2%
0.202 40
 
0.2%
0.703 39
 
0.2%
Other values (3198) 18243
96.5%
(Missing) 88
 
0.5%
ValueCountFrequency (%)
0.005 1
 
< 0.1%
0.007 1
 
< 0.1%
0.009 2
< 0.1%
0.011 1
 
< 0.1%
0.013 4
< 0.1%
0.015 1
 
< 0.1%
0.018 2
< 0.1%
0.019 2
< 0.1%
0.022 4
< 0.1%
0.023 3
< 0.1%
ValueCountFrequency (%)
4.996 1
< 0.1%
4.989 1
< 0.1%
4.933 1
< 0.1%
4.872 1
< 0.1%
4.765 1
< 0.1%
4.756 1
< 0.1%
4.751 1
< 0.1%
4.742 1
< 0.1%
4.735 1
< 0.1%
4.724 1
< 0.1%

postOfficeDistance
Real number (ℝ)

HIGH CORRELATION 

Distinct1869
Distinct (%)9.9%
Missing25
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.51874804
Minimum0.001
Maximum4.967
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2024-03-17T17:01:58.508935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.094
Q10.237
median0.387
Q30.621
95-th percentile1.348
Maximum4.967
Range4.966
Interquartile range (IQR)0.384

Descriptive statistics

Standard deviation0.5067833
Coefficient of variation (CV)0.97693535
Kurtosis19.212354
Mean0.51874804
Median Absolute Deviation (MAD)0.176
Skewness3.6521344
Sum9793.963
Variance0.25682931
MonotonicityNot monotonic
2024-03-17T17:01:58.618045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.351 173
 
0.9%
0.299 68
 
0.4%
0.182 62
 
0.3%
0.346 56
 
0.3%
0.217 55
 
0.3%
0.207 55
 
0.3%
0.352 54
 
0.3%
0.449 53
 
0.3%
0.227 52
 
0.3%
0.269 51
 
0.3%
Other values (1859) 18201
96.3%
ValueCountFrequency (%)
0.001 1
 
< 0.1%
0.002 1
 
< 0.1%
0.003 2
 
< 0.1%
0.004 3
 
< 0.1%
0.005 1
 
< 0.1%
0.006 4
 
< 0.1%
0.008 2
 
< 0.1%
0.009 2
 
< 0.1%
0.01 22
0.1%
0.011 3
 
< 0.1%
ValueCountFrequency (%)
4.967 1
 
< 0.1%
4.905 1
 
< 0.1%
4.889 1
 
< 0.1%
4.801 2
< 0.1%
4.798 1
 
< 0.1%
4.745 1
 
< 0.1%
4.707 3
< 0.1%
4.704 1
 
< 0.1%
4.692 1
 
< 0.1%
4.691 1
 
< 0.1%

kindergartenDistance
Real number (ℝ)

Distinct1481
Distinct (%)7.8%
Missing13
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.3710334
Minimum0.004
Maximum4.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2024-03-17T17:01:58.724087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.004
5-th percentile0.066
Q10.158
median0.264
Q30.415
95-th percentile0.914
Maximum4.96
Range4.956
Interquartile range (IQR)0.257

Descriptive statistics

Standard deviation0.45233823
Coefficient of variation (CV)1.2191308
Kurtosis31.14531
Mean0.3710334
Median Absolute Deviation (MAD)0.123
Skewness4.9182381
Sum7009.563
Variance0.20460987
MonotonicityNot monotonic
2024-03-17T17:01:58.834342image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.405 139
 
0.7%
0.119 93
 
0.5%
0.164 92
 
0.5%
0.216 78
 
0.4%
0.172 74
 
0.4%
0.313 71
 
0.4%
0.157 69
 
0.4%
0.117 68
 
0.4%
0.112 68
 
0.4%
0.261 68
 
0.4%
Other values (1471) 18072
95.6%
ValueCountFrequency (%)
0.004 2
 
< 0.1%
0.005 8
< 0.1%
0.006 6
< 0.1%
0.008 1
 
< 0.1%
0.009 6
< 0.1%
0.01 2
 
< 0.1%
0.011 2
 
< 0.1%
0.012 3
 
< 0.1%
0.013 5
< 0.1%
0.014 4
< 0.1%
ValueCountFrequency (%)
4.96 2
< 0.1%
4.698 1
< 0.1%
4.695 1
< 0.1%
4.662 2
< 0.1%
4.659 1
< 0.1%
4.608 1
< 0.1%
4.594 1
< 0.1%
4.371 1
< 0.1%
4.347 1
< 0.1%
4.342 1
< 0.1%

restaurantDistance
Real number (ℝ)

HIGH CORRELATION 

Distinct1556
Distinct (%)8.3%
Missing58
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean0.35664615
Minimum0.001
Maximum4.985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2024-03-17T17:01:58.942723image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.037
Q10.1185
median0.235
Q30.417
95-th percentile1.01
Maximum4.985
Range4.984
Interquartile range (IQR)0.2985

Descriptive statistics

Standard deviation0.47588441
Coefficient of variation (CV)1.3343321
Kurtosis30.60693
Mean0.35664615
Median Absolute Deviation (MAD)0.138
Skewness4.7697782
Sum6721.71
Variance0.22646597
MonotonicityNot monotonic
2024-03-17T17:01:59.050741image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.058 163
 
0.9%
0.08 99
 
0.5%
0.059 84
 
0.4%
0.162 79
 
0.4%
0.067 79
 
0.4%
0.123 76
 
0.4%
0.235 70
 
0.4%
0.088 68
 
0.4%
0.062 66
 
0.3%
0.141 64
 
0.3%
Other values (1546) 17999
95.2%
ValueCountFrequency (%)
0.001 2
 
< 0.1%
0.002 1
 
< 0.1%
0.003 5
 
< 0.1%
0.004 16
0.1%
0.005 12
0.1%
0.006 6
 
< 0.1%
0.007 12
0.1%
0.008 20
0.1%
0.009 27
0.1%
0.01 18
0.1%
ValueCountFrequency (%)
4.985 1
 
< 0.1%
4.971 1
 
< 0.1%
4.928 2
< 0.1%
4.925 1
 
< 0.1%
4.806 2
< 0.1%
4.718 1
 
< 0.1%
4.715 1
 
< 0.1%
4.702 1
 
< 0.1%
4.696 3
< 0.1%
4.687 1
 
< 0.1%

collegeDistance
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3888
Distinct (%)21.2%
Missing565
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean1.4600467
Minimum0.006
Maximum4.998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2024-03-17T17:01:59.154897image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.006
5-th percentile0.205
Q10.592
median1.14
Q32.107
95-th percentile3.743
Maximum4.998
Range4.992
Interquartile range (IQR)1.515

Descriptive statistics

Standard deviation1.1028467
Coefficient of variation (CV)0.75535026
Kurtosis0.31844283
Mean1.4600467
Median Absolute Deviation (MAD)0.6665
Skewness0.99571572
Sum26777.257
Variance1.2162708
MonotonicityNot monotonic
2024-03-17T17:01:59.265217image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.622 128
 
0.7%
0.904 45
 
0.2%
0.893 44
 
0.2%
0.861 43
 
0.2%
1.121 42
 
0.2%
0.229 39
 
0.2%
0.523 38
 
0.2%
1.139 38
 
0.2%
2.017 37
 
0.2%
0.335 37
 
0.2%
Other values (3878) 17849
94.4%
(Missing) 565
 
3.0%
ValueCountFrequency (%)
0.006 1
< 0.1%
0.013 1
< 0.1%
0.018 1
< 0.1%
0.019 1
< 0.1%
0.022 2
< 0.1%
0.024 1
< 0.1%
0.026 1
< 0.1%
0.028 1
< 0.1%
0.03 1
< 0.1%
0.031 2
< 0.1%
ValueCountFrequency (%)
4.998 1
 
< 0.1%
4.997 1
 
< 0.1%
4.986 1
 
< 0.1%
4.981 1
 
< 0.1%
4.977 3
< 0.1%
4.975 1
 
< 0.1%
4.973 1
 
< 0.1%
4.968 1
 
< 0.1%
4.962 1
 
< 0.1%
4.96 2
< 0.1%

pharmacyDistance
Real number (ℝ)

HIGH CORRELATION 

Distinct1524
Distinct (%)8.1%
Missing27
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.36413397
Minimum0.003
Maximum4.992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2024-03-17T17:01:59.369991image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile0.057
Q10.145
median0.239
Q30.405
95-th percentile0.98815
Maximum4.992
Range4.989
Interquartile range (IQR)0.26

Descriptive statistics

Standard deviation0.46933442
Coefficient of variation (CV)1.2889059
Kurtosis30.459531
Mean0.36413397
Median Absolute Deviation (MAD)0.116
Skewness4.8168757
Sum6874.121
Variance0.2202748
MonotonicityNot monotonic
2024-03-17T17:01:59.478560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.205 154
 
0.8%
0.142 101
 
0.5%
0.155 101
 
0.5%
0.146 100
 
0.5%
0.166 91
 
0.5%
0.108 83
 
0.4%
0.259 83
 
0.4%
0.189 80
 
0.4%
0.157 76
 
0.4%
0.079 75
 
0.4%
Other values (1514) 17934
94.9%
ValueCountFrequency (%)
0.003 3
 
< 0.1%
0.004 5
< 0.1%
0.005 2
 
< 0.1%
0.006 5
< 0.1%
0.007 5
< 0.1%
0.008 9
< 0.1%
0.009 9
< 0.1%
0.01 7
< 0.1%
0.011 6
< 0.1%
0.012 4
< 0.1%
ValueCountFrequency (%)
4.992 1
< 0.1%
4.955 1
< 0.1%
4.861 1
< 0.1%
4.847 1
< 0.1%
4.802 2
< 0.1%
4.799 1
< 0.1%
4.655 1
< 0.1%
4.636 1
< 0.1%
4.618 2
< 0.1%
4.595 1
< 0.1%

ownership
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.8 KiB
condominium
16886 
cooperative
2019 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters207955
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcondominium
2nd rowcooperative
3rd rowcondominium
4th rowcondominium
5th rowcondominium

Common Values

ValueCountFrequency (%)
condominium 16886
89.3%
cooperative 2019
 
10.7%

Length

2024-03-17T17:01:59.574601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-17T17:01:59.649408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
condominium 16886
89.3%
cooperative 2019
 
10.7%

Most occurring characters

ValueCountFrequency (%)
o 37810
18.2%
i 35791
17.2%
n 33772
16.2%
m 33772
16.2%
c 18905
9.1%
d 16886
8.1%
u 16886
8.1%
e 4038
 
1.9%
p 2019
 
1.0%
r 2019
 
1.0%
Other values (3) 6057
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 207955
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 37810
18.2%
i 35791
17.2%
n 33772
16.2%
m 33772
16.2%
c 18905
9.1%
d 16886
8.1%
u 16886
8.1%
e 4038
 
1.9%
p 2019
 
1.0%
r 2019
 
1.0%
Other values (3) 6057
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 207955
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 37810
18.2%
i 35791
17.2%
n 33772
16.2%
m 33772
16.2%
c 18905
9.1%
d 16886
8.1%
u 16886
8.1%
e 4038
 
1.9%
p 2019
 
1.0%
r 2019
 
1.0%
Other values (3) 6057
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 207955
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 37810
18.2%
i 35791
17.2%
n 33772
16.2%
m 33772
16.2%
c 18905
9.1%
d 16886
8.1%
u 16886
8.1%
e 4038
 
1.9%
p 2019
 
1.0%
r 2019
 
1.0%
Other values (3) 6057
 
2.9%

buildingMaterial
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing7387
Missing (%)39.1%
Memory size147.8 KiB
brick
8804 
concreteSlab
2714 

Length

Max length12
Median length5
Mean length6.6494183
Min length5

Characters and Unicode

Total characters76588
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconcreteSlab
2nd rowconcreteSlab
3rd rowbrick
4th rowbrick
5th rowconcreteSlab

Common Values

ValueCountFrequency (%)
brick 8804
46.6%
concreteSlab 2714
 
14.4%
(Missing) 7387
39.1%

Length

2024-03-17T17:01:59.733354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-17T17:01:59.810156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
brick 8804
76.4%
concreteslab 2714
 
23.6%

Most occurring characters

ValueCountFrequency (%)
c 14232
18.6%
b 11518
15.0%
r 11518
15.0%
i 8804
11.5%
k 8804
11.5%
e 5428
 
7.1%
o 2714
 
3.5%
n 2714
 
3.5%
t 2714
 
3.5%
S 2714
 
3.5%
Other values (2) 5428
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 73874
96.5%
Uppercase Letter 2714
 
3.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 14232
19.3%
b 11518
15.6%
r 11518
15.6%
i 8804
11.9%
k 8804
11.9%
e 5428
 
7.3%
o 2714
 
3.7%
n 2714
 
3.7%
t 2714
 
3.7%
l 2714
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
S 2714
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 76588
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 14232
18.6%
b 11518
15.0%
r 11518
15.0%
i 8804
11.5%
k 8804
11.5%
e 5428
 
7.1%
o 2714
 
3.5%
n 2714
 
3.5%
t 2714
 
3.5%
S 2714
 
3.5%
Other values (2) 5428
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 76588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 14232
18.6%
b 11518
15.0%
r 11518
15.0%
i 8804
11.5%
k 8804
11.5%
e 5428
 
7.1%
o 2714
 
3.5%
n 2714
 
3.5%
t 2714
 
3.5%
S 2714
 
3.5%
Other values (2) 5428
 
7.1%

condition
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing14344
Missing (%)75.9%
Memory size147.8 KiB
premium
2426 
low
2135 

Length

Max length7
Median length7
Mean length5.1276036
Min length3

Characters and Unicode

Total characters23387
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlow
2nd rowpremium
3rd rowlow
4th rowlow
5th rowpremium

Common Values

ValueCountFrequency (%)
premium 2426
 
12.8%
low 2135
 
11.3%
(Missing) 14344
75.9%

Length

2024-03-17T17:01:59.896428image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-17T17:01:59.972092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
premium 2426
53.2%
low 2135
46.8%

Most occurring characters

ValueCountFrequency (%)
m 4852
20.7%
p 2426
10.4%
r 2426
10.4%
e 2426
10.4%
i 2426
10.4%
u 2426
10.4%
l 2135
9.1%
o 2135
9.1%
w 2135
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23387
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 4852
20.7%
p 2426
10.4%
r 2426
10.4%
e 2426
10.4%
i 2426
10.4%
u 2426
10.4%
l 2135
9.1%
o 2135
9.1%
w 2135
9.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 23387
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 4852
20.7%
p 2426
10.4%
r 2426
10.4%
e 2426
10.4%
i 2426
10.4%
u 2426
10.4%
l 2135
9.1%
o 2135
9.1%
w 2135
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 4852
20.7%
p 2426
10.4%
r 2426
10.4%
e 2426
10.4%
i 2426
10.4%
u 2426
10.4%
l 2135
9.1%
o 2135
9.1%
w 2135
9.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
False
14017 
True
4888 
ValueCountFrequency (%)
False 14017
74.1%
True 4888
 
25.9%
2024-03-17T17:02:00.038059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

hasBalcony
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
True
10864 
False
8041 
ValueCountFrequency (%)
True 10864
57.5%
False 8041
42.5%
2024-03-17T17:02:00.106267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

hasElevator
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing926
Missing (%)4.9%
Memory size37.0 KiB
False
9510 
True
8469 
(Missing)
 
926
ValueCountFrequency (%)
False 9510
50.3%
True 8469
44.8%
(Missing) 926
 
4.9%
2024-03-17T17:02:00.176345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

hasSecurity
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
False
16977 
True
1928 
ValueCountFrequency (%)
False 16977
89.8%
True 1928
 
10.2%
2024-03-17T17:02:00.246636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
False
10159 
True
8746 
ValueCountFrequency (%)
False 10159
53.7%
True 8746
46.3%
2024-03-17T17:02:00.315092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

price
Real number (ℝ)

HIGH CORRELATION 

Distinct2733
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean692559.22
Minimum150000
Maximum2500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2024-03-17T17:02:00.402828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum150000
5-th percentile286500
Q1458800
median620000
Q3830000
95-th percentile1390000
Maximum2500000
Range2350000
Interquartile range (IQR)371200

Descriptive statistics

Standard deviation344389.01
Coefficient of variation (CV)0.49727012
Kurtosis3.4004073
Mean692559.22
Median Absolute Deviation (MAD)179000
Skewness1.5560085
Sum1.3092832 × 1010
Variance1.1860379 × 1011
MonotonicityNot monotonic
2024-03-17T17:02:00.932545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
599000 288
 
1.5%
699000 246
 
1.3%
499000 214
 
1.1%
650000 203
 
1.1%
799000 194
 
1.0%
399000 173
 
0.9%
550000 164
 
0.9%
899000 164
 
0.9%
750000 159
 
0.8%
649000 141
 
0.7%
Other values (2723) 16959
89.7%
ValueCountFrequency (%)
150000 2
< 0.1%
151200 1
< 0.1%
154040 1
< 0.1%
154900 1
< 0.1%
159900 1
< 0.1%
160000 1
< 0.1%
162500 1
< 0.1%
164900 1
< 0.1%
165000 1
< 0.1%
169000 2
< 0.1%
ValueCountFrequency (%)
2500000 8
< 0.1%
2499999 1
 
< 0.1%
2499000 4
< 0.1%
2490000 2
 
< 0.1%
2485350 1
 
< 0.1%
2484000 1
 
< 0.1%
2480000 1
 
< 0.1%
2456000 1
 
< 0.1%
2450000 3
 
< 0.1%
2400000 3
 
< 0.1%

Interactions

2024-03-17T17:01:53.216271image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:32.872280image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:34.198219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:35.406790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:36.761172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:38.044638image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:39.476083image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:40.670790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:41.828608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:43.297327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:44.484447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:45.699399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:46.906905image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:48.405837image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:49.600826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:50.812554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:52.010352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:53.287719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:32.951122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:34.277916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:35.480251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:36.837464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:38.120765image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:39.549649image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:40.740409image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:41.903630image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:43.368662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:44.558481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:45.774702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:46.978598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:48.478302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:49.673606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:50.887581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:52.082464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:53.355649image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:33.020189image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:34.348077image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:35.550749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:36.920215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:38.192535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:39.617710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:40.810694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:41.975590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:43.436565image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:44.630222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:45.846114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:47.046879image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:48.547178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:49.743509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:50.954579image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:52.151477image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:53.427815image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:33.092733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:34.418429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:35.625358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:36.993232image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:38.271689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:39.689024image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:40.880580image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:42.053076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:43.507965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:44.702689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:45.917696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:47.127782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:48.618735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:49.820593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:51.027065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:52.224497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2024-03-17T17:01:51.875801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:53.076627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:54.674981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:34.126868image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:35.325373image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:36.688422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:37.968564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:39.404084image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:40.602171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:41.760577image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:43.019753image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:44.414455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:45.626641image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:46.839677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:48.337346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:49.532500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:50.738033image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:51.942077image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-17T17:01:53.146776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2024-03-17T17:02:01.028194image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
squareMetersroomsfloorfloorCountbuildYearlatitudelongitudecentreDistancepoiCountschoolDistanceclinicDistancepostOfficeDistancekindergartenDistancerestaurantDistancecollegeDistancepharmacyDistancepricecitytypeownershipbuildingMaterialconditionhasParkingSpacehasBalconyhasElevatorhasSecurityhasStorageRoom
squareMeters1.0000.825-0.083-0.1400.0760.0410.0020.086-0.0490.0940.0210.0380.0680.0120.0600.0420.6280.0570.1460.0550.2200.0700.1270.1510.0880.0760.053
rooms0.8251.000-0.041-0.0820.0590.0350.0140.086-0.0660.0700.0480.0330.0460.0450.0680.0440.4990.0620.0920.0130.0970.0000.0710.1510.0830.0310.043
floor-0.083-0.0411.0000.555-0.0060.0060.059-0.0260.116-0.102-0.100-0.082-0.109-0.076-0.042-0.125-0.0380.0510.1470.1350.3470.0810.0220.0570.3510.0380.059
floorCount-0.140-0.0820.5551.0000.107-0.0300.117-0.0430.184-0.123-0.184-0.113-0.165-0.138-0.054-0.1650.0350.1040.2520.2310.5660.1700.0930.1240.5350.1070.174
buildYear0.0760.059-0.0060.1071.000-0.0430.0550.366-0.4290.3940.2490.3130.1540.2270.3960.3130.2430.1250.7120.3590.7640.5090.2630.3270.4660.2630.483
latitude0.0410.0350.006-0.030-0.0431.000-0.2180.083-0.0640.0610.0420.0260.1020.0360.0840.0430.0090.9630.1470.1480.2010.1720.1650.0730.2460.1580.147
longitude0.0020.0140.0590.1170.055-0.2181.0000.1850.120-0.057-0.100-0.017-0.164-0.123-0.024-0.0450.1990.9370.1500.1530.1630.1500.1620.0990.2200.1470.135
centreDistance0.0860.086-0.026-0.0430.3660.0830.1851.000-0.5130.3820.4390.2760.0770.3210.5910.2950.1360.1860.3020.0610.2040.1220.1250.1720.1210.1480.142
poiCount-0.049-0.0660.1160.184-0.429-0.0640.120-0.5131.000-0.595-0.583-0.551-0.412-0.686-0.611-0.6080.1060.0930.3050.0440.1650.0700.0710.1760.0680.0490.080
schoolDistance0.0940.070-0.102-0.1230.3940.061-0.0570.382-0.5951.0000.3780.3870.3760.3380.4230.4010.0780.1940.1560.0980.1350.2170.0930.0960.0780.1130.192
clinicDistance0.0210.048-0.100-0.1840.2490.042-0.1000.439-0.5830.3781.0000.3140.1980.4160.4140.366-0.1240.1630.1790.0610.0860.1130.0480.1220.0530.0710.084
postOfficeDistance0.0380.033-0.082-0.1130.3130.026-0.0170.276-0.5510.3870.3141.0000.2720.4220.2820.4660.0080.1760.1540.0780.0930.1610.0840.0850.0730.1020.143
kindergartenDistance0.0680.046-0.109-0.1650.1540.102-0.1640.077-0.4120.3760.1980.2721.0000.2370.2020.3430.0090.1900.0630.0720.1250.0980.0390.0320.0590.0400.103
restaurantDistance0.0120.045-0.076-0.1380.2270.036-0.1230.321-0.6860.3380.4160.4220.2371.0000.3530.477-0.1680.1890.0930.0440.0650.0920.0580.0490.0730.0650.087
collegeDistance0.0600.068-0.042-0.0540.3960.084-0.0240.591-0.6110.4230.4140.2820.2020.3531.0000.3300.0260.1150.2590.0530.1080.1260.0880.1560.1070.1020.130
pharmacyDistance0.0420.044-0.125-0.1650.3130.043-0.0450.295-0.6080.4010.3660.4660.3430.4770.3301.0000.0110.1830.1110.0700.1110.1420.0680.0530.0730.0970.141
price0.6280.499-0.0380.0350.2430.0090.1990.1360.1060.078-0.1240.0080.009-0.1680.0260.0111.0000.2400.2530.1470.3440.3430.1500.1090.2080.1620.173
city0.0570.0620.0510.1040.1250.9630.9370.1860.0930.1940.1630.1760.1900.1890.1150.1830.2401.0000.1840.1830.2260.2030.1740.1310.2530.1710.182
type0.1460.0920.1470.2520.7120.1470.1500.3020.3050.1560.1790.1540.0630.0930.2590.1110.2530.1841.0000.2420.5880.3600.2170.3410.4420.2120.318
ownership0.0550.0130.1350.2310.3590.1480.1530.0610.0440.0980.0610.0780.0720.0440.0530.0700.1470.1830.2421.0000.3730.2160.0000.0510.0170.0820.163
buildingMaterial0.2200.0970.3470.5660.7640.2010.1630.2040.1650.1350.0860.0930.1250.0650.1080.1110.3440.2260.5880.3731.0000.2640.1390.1200.1100.1730.262
condition0.0700.0000.0810.1700.5090.1720.1500.1220.0700.2170.1130.1610.0980.0920.1260.1420.3430.2030.3600.2160.2641.0000.2620.0000.2120.0070.254
hasParkingSpace0.1270.0710.0220.0930.2630.1650.1620.1250.0710.0930.0480.0840.0390.0580.0880.0680.1500.1740.2170.0000.1390.2621.0000.0710.1110.0780.027
hasBalcony0.1510.1510.0570.1240.3270.0730.0990.1720.1760.0960.1220.0850.0320.0490.1560.0530.1090.1310.3410.0510.1200.0000.0711.0000.1230.0730.092
hasElevator0.0880.0830.3510.5350.4660.2460.2200.1210.0680.0780.0530.0730.0590.0730.1070.0730.2080.2530.4420.0170.1100.2120.1110.1231.0000.1360.262
hasSecurity0.0760.0310.0380.1070.2630.1580.1470.1480.0490.1130.0710.1020.0400.0650.1020.0970.1620.1710.2120.0820.1730.0070.0780.0730.1361.0000.129
hasStorageRoom0.0530.0430.0590.1740.4830.1470.1350.1420.0800.1920.0840.1430.1030.0870.1300.1410.1730.1820.3180.1630.2620.2540.0270.0920.2620.1291.000

Missing values

2024-03-17T17:01:54.804034image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-17T17:01:55.075599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-17T17:01:55.326175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idcitytypesquareMetersroomsfloorfloorCountbuildYearlatitudelongitudecentreDistancepoiCountschoolDistanceclinicDistancepostOfficeDistancekindergartenDistancerestaurantDistancecollegeDistancepharmacyDistanceownershipbuildingMaterialconditionhasParkingSpacehasBalconyhasElevatorhasSecurityhasStorageRoomprice
0f8524536d4b09a0c8ccc0197ec9d7bdeszczecinblockOfFlats63.003.04.010.01980.053.37893314.6252966.539.00.1181.3890.6280.1051.652NaN0.413condominiumconcreteSlabNaNyesyesyesnoyes415000
1accbe77d4b360fea9735f138a50608ddszczecinblockOfFlats36.002.08.010.0NaN53.44269214.5596902.1516.00.2730.4920.6520.2910.3481.4040.205cooperativeconcreteSlabNaNnoyesyesnoyes395995
28373aa373dbc3fe7ca3b7434166b8766szczecintenement73.023.02.03.0NaN53.45222214.5533333.249.00.2750.6720.3670.2460.3001.8570.280condominiumbrickNaNnonononono565000
30a68cd14c44ec5140143ece75d739535szczecintenement87.603.02.03.0NaN53.43510014.5329002.2732.00.1750.2590.2230.3590.1010.3100.087condominiumbrickNaNyesyesnonoyes640000
4f66320e153c2441edc0fe293b54c8aebszczecinblockOfFlats66.003.01.03.0NaN53.41027814.5036114.071.00.2181.6900.5040.7040.5012.1380.514condominiumNaNNaNnonononono759000
52e190fcd6934978ca36d86ba41e842fcszczecinblockOfFlats63.303.02.04.01997.053.46310014.5728004.4810.00.0791.2240.7370.2601.1020.3770.745cooperativeconcreteSlabNaNyesyesnonoyes499000
6ec27024bfcd012728617a35dad2cb6b8szczecinblockOfFlats47.452.02.010.01974.053.45023214.5626252.9918.00.3270.3780.2340.2620.2441.7360.277condominiumconcreteSlablownonoyesnoyes370000
7d3e0e36529df3360849ec40168c10755szczecinapartmentBuilding60.082.03.04.02009.053.45468514.5515203.538.00.5720.7840.3050.4350.2571.9450.155condominiumbrickpremiumnoyesyesnono629000
87e1981e920d763d6237c5bdcf13cf5b7szczecinblockOfFlats47.762.08.012.01980.053.45886914.5364034.276.00.3450.5200.3360.5000.2651.8790.420condominiumconcreteSlabNaNnoyesyesnoyes399000
94a04a9c54d8281e3ec23df031e538d85szczecintenement72.094.02.03.01890.053.43509214.5596121.3022.00.2320.2920.3560.3010.1990.6530.199condominiumbricklowyesnononoyes325000
idcitytypesquareMetersroomsfloorfloorCountbuildYearlatitudelongitudecentreDistancepoiCountschoolDistanceclinicDistancepostOfficeDistancekindergartenDistancerestaurantDistancecollegeDistancepharmacyDistanceownershipbuildingMaterialconditionhasParkingSpacehasBalconyhasElevatorhasSecurityhasStorageRoomprice
18895908fef6a182cef3ce3fa6a8ba9c34ed8bydgoszcztenement110.074.02.02.01852.053.12600018.0079000.6554.00.1110.2050.3510.4050.0580.6220.205condominiumbricklownonononono510000
18896252d9a7cfee03c757c16ee6a98228327bydgoszczblockOfFlats93.304.0NaN2.01937.053.12120017.9925000.5629.00.3010.4550.8010.3130.1230.2680.300condominiumbrickpremiumnonononoyes469900
18897a1f6f42225389974c8665e23e662a20bbydgoszczNaN71.693.01.02.0NaN53.11292717.9756301.967.00.0561.0980.4480.2980.6650.9850.164condominiumbrickNaNyesnononono467000
18898b54751e18d2395c2ad51071951bec6f5bydgoszcztenement47.002.0NaN1.0NaN53.12896017.9988700.7829.00.4390.8060.3470.3370.1320.3280.161condominiumbrickNaNnonononono260000
18899bb19da639a2de8bba49be2ca49053c87bydgoszcztenement108.965.02.04.01889.053.12600018.0079000.6554.00.1110.2050.3510.4050.0580.6220.205condominiumbrickNaNnonononoyes795000
18900ae88d7ea0a7a5fa0e9d287cfd78e5676bydgoszczblockOfFlats84.003.0NaN4.02000.053.11292717.9756301.967.00.0561.0980.4480.2980.6650.9850.164cooperativebrickNaNyesyesnonoyes789000
189016a4b30b5fcdee00bfe5bcc0da82df9b1bydgoszczblockOfFlats94.304.0NaN2.01998.053.11592117.9563963.049.00.3782.2730.6050.1170.0880.7590.428condominiumNaNNaNnoyesnonoyes699000
189021e7f4f1fdfea31eb84e071d697839632bydgoszczNaN50.122.01.01.0NaN53.12600018.0079000.6554.00.1110.2050.3510.4050.0580.6220.205condominiumbrickNaNyesnononono360000
18903d971c4ed3aab9919bf203e96019a678ebydgoszcztenement100.004.03.04.01910.053.12600018.0079000.6554.00.1110.2050.3510.4050.0580.6220.205condominiumbrickpremiumyesyesnonoyes590000
18904cd0241b70b79aaaf767a0dd3a7cfbb31bydgoszczNaN81.075.01.04.02019.053.11592117.9563963.049.00.3782.2730.6050.1170.0880.7590.428condominiumNaNNaNnoyesyesnono699000